Overview

Dataset statistics

Number of variables32
Number of observations736
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory100.8 KiB
Average record size in memory140.2 B

Variable types

Categorical26
Numeric6

Alerts

AgeCategory is highly overall correlated with agecatakHigh correlation
BMI_category is highly overall correlated with weightst1High correlation
HTTLPRrs35531 is highly overall correlated with httlpr1 and 1 other fieldsHigh correlation
Rs10482605 is highly overall correlated with Rs1360780 and 3 other fieldsHigh correlation
Rs1360780 is highly overall correlated with Rs10482605 and 3 other fieldsHigh correlation
agecatak is highly overall correlated with AgeCategoryHigh correlation
heightst1 is highly overall correlated with weightst1High correlation
httlpr1 is highly overall correlated with HTTLPRrs35531High correlation
rs1386494 is highly overall correlated with Rs10482605 and 3 other fieldsHigh correlation
rs1843809 is highly overall correlated with Rs10482605 and 3 other fieldsHigh correlation
rs34517220 is highly overall correlated with Rs10482605 and 3 other fieldsHigh correlation
rs35531 is highly overall correlated with HTTLPRrs35531High correlation
weightst1 is highly overall correlated with BMI_category and 1 other fieldsHigh correlation
childeduc1 is highly imbalanced (62.9%)Imbalance
BMI_category is highly imbalanced (55.6%)Imbalance
livelihood1 is highly imbalanced (73.6%)Imbalance
sexualever1 is highly imbalanced (92.2%)Imbalance
childartk1 is highly imbalanced (76.0%)Imbalance
childpremt1 is highly imbalanced (74.3%)Imbalance
chilborhiv is highly imbalanced (79.7%)Imbalance
GroupCategory is uniformly distributedUniform
religion1 has 77 (10.5%) zerosZeros
childstay1 has 207 (28.1%) zerosZeros
HTTLPRrs35531 has 317 (43.1%) zerosZeros

Reproduction

Analysis started2024-03-25 11:59:21.883499
Analysis finished2024-03-25 11:59:33.690104
Duration11.81 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

agecatak
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
1
267 
2
259 
0
210 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 267
36.3%
2 259
35.2%
0 210
28.5%

Length

2024-03-25T14:59:34.058246image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:34.220089image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 267
36.3%
2 259
35.2%
0 210
28.5%

Most occurring characters

ValueCountFrequency (%)
1 267
36.3%
2 259
35.2%
0 210
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 267
36.3%
2 259
35.2%
0 210
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 267
36.3%
2 259
35.2%
0 210
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 267
36.3%
2 259
35.2%
0 210
28.5%

AgeCategory
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
469 
1
267 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 469
63.7%
1 267
36.3%

Length

2024-03-25T14:59:34.406752image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:34.560923image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 469
63.7%
1 267
36.3%

Most occurring characters

ValueCountFrequency (%)
0 469
63.7%
1 267
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 469
63.7%
1 267
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 469
63.7%
1 267
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 469
63.7%
1 267
36.3%

sex1
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
394 
1
342 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 394
53.5%
1 342
46.5%

Length

2024-03-25T14:59:34.720950image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:35.202795image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 394
53.5%
1 342
46.5%

Most occurring characters

ValueCountFrequency (%)
0 394
53.5%
1 342
46.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 394
53.5%
1 342
46.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 394
53.5%
1 342
46.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 394
53.5%
1 342
46.5%

religion1
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.732337
Minimum0
Maximum5
Zeros77
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-03-25T14:59:35.354701image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3042424
Coefficient of variation (CV)0.7528803
Kurtosis-0.42018608
Mean1.732337
Median Absolute Deviation (MAD)1
Skewness0.86277591
Sum1275
Variance1.7010481
MonotonicityNot monotonic
2024-03-25T14:59:35.516928image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 353
48.0%
2 154
20.9%
4 142
19.3%
0 77
 
10.5%
5 8
 
1.1%
3 2
 
0.3%
ValueCountFrequency (%)
0 77
 
10.5%
1 353
48.0%
2 154
20.9%
3 2
 
0.3%
4 142
19.3%
5 8
 
1.1%
ValueCountFrequency (%)
5 8
 
1.1%
4 142
19.3%
3 2
 
0.3%
2 154
20.9%
1 353
48.0%
0 77
 
10.5%

childeduc1
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
1
651 
2
72 
0
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 651
88.5%
2 72
 
9.8%
0 13
 
1.8%

Length

2024-03-25T14:59:35.819486image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:35.974304image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 651
88.5%
2 72
 
9.8%
0 13
 
1.8%

Most occurring characters

ValueCountFrequency (%)
1 651
88.5%
2 72
 
9.8%
0 13
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 651
88.5%
2 72
 
9.8%
0 13
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 651
88.5%
2 72
 
9.8%
0 13
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 651
88.5%
2 72
 
9.8%
0 13
 
1.8%

heightst1
Real number (ℝ)

HIGH CORRELATION 

Distinct319
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28587144
Minimum0
Maximum1
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-03-25T14:59:36.188703image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.22021944
Q10.25681215
median0.28574873
Q30.3170967
95-th percentile0.35326742
Maximum1
Range1
Interquartile range (IQR)0.060284543

Descriptive statistics

Standard deviation0.054604204
Coefficient of variation (CV)0.19100965
Kurtosis44.93635
Mean0.28587144
Median Absolute Deviation (MAD)0.030021702
Skewness2.6858758
Sum210.40138
Variance0.0029816191
MonotonicityNot monotonic
2024-03-25T14:59:36.504782image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3170966964 12
 
1.6%
0.3002170244 11
 
1.5%
0.2568121534 11
 
1.5%
0.2736918254 10
 
1.4%
0.268869062 9
 
1.2%
0.2881601157 9
 
1.2%
0.2905714975 9
 
1.2%
0.2640462985 9
 
1.2%
0.312273933 8
 
1.1%
0.2953942609 8
 
1.1%
Other values (309) 640
87.0%
ValueCountFrequency (%)
0 1
0.1%
0.001687967205 1
0.1%
0.003134796238 1
0.1%
0.01446829033 1
0.1%
0.05425608874 1
0.1%
0.1704846877 1
0.1%
0.190499156 1
0.1%
0.1965276103 1
0.1%
0.1970098867 1
0.1%
0.2025560646 1
0.1%
ValueCountFrequency (%)
1 1
0.1%
0.649867374 1
0.1%
0.4400771642 1
0.1%
0.4072823728 1
0.1%
0.4036653002 1
0.1%
0.3942609115 1
0.1%
0.3870267663 2
0.3%
0.3814805884 1
0.1%
0.3754521341 1
0.1%
0.3730407524 1
0.1%

weightst1
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13900777
Minimum0
Maximum1
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-03-25T14:59:36.752986image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.041958042
Q10.076923077
median0.11888112
Q30.18181818
95-th percentile0.28671329
Maximum1
Range1
Interquartile range (IQR)0.1048951

Descriptive statistics

Standard deviation0.083324114
Coefficient of variation (CV)0.59942054
Kurtosis15.297716
Mean0.13900777
Median Absolute Deviation (MAD)0.048951049
Skewness2.251221
Sum102.30972
Variance0.006942908
MonotonicityNot monotonic
2024-03-25T14:59:36.968040image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.07692307692 39
 
5.3%
0.06993006993 36
 
4.9%
0.09090909091 34
 
4.6%
0.0979020979 30
 
4.1%
0.1398601399 29
 
3.9%
0.1188811189 29
 
3.9%
0.1258741259 28
 
3.8%
0.1048951049 27
 
3.7%
0.1748251748 24
 
3.3%
0.08391608392 24
 
3.3%
Other values (82) 436
59.2%
ValueCountFrequency (%)
0 1
 
0.1%
0.006993006993 1
 
0.1%
0.01398601399 3
 
0.4%
0.02797202797 7
 
1.0%
0.03496503497 8
 
1.1%
0.04195804196 20
2.7%
0.04335664336 1
 
0.1%
0.04545454545 2
 
0.3%
0.04895104895 21
2.9%
0.05244755245 2
 
0.3%
ValueCountFrequency (%)
1 1
 
0.1%
0.4265734266 1
 
0.1%
0.4125874126 2
0.3%
0.3916083916 1
 
0.1%
0.3846153846 2
0.3%
0.3706293706 2
0.3%
0.3636363636 1
 
0.1%
0.3566433566 1
 
0.1%
0.3496503497 2
0.3%
0.3426573427 3
0.4%

BMI_category
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
3
589 
0
121 
2
 
17
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row0
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 589
80.0%
0 121
 
16.4%
2 17
 
2.3%
1 9
 
1.2%

Length

2024-03-25T14:59:37.170528image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:37.339026image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
3 589
80.0%
0 121
 
16.4%
2 17
 
2.3%
1 9
 
1.2%

Most occurring characters

ValueCountFrequency (%)
3 589
80.0%
0 121
 
16.4%
2 17
 
2.3%
1 9
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 589
80.0%
0 121
 
16.4%
2 17
 
2.3%
1 9
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 589
80.0%
0 121
 
16.4%
2 17
 
2.3%
1 9
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 589
80.0%
0 121
 
16.4%
2 17
 
2.3%
1 9
 
1.2%

childtrib1
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
526 
1
204 
2
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 526
71.5%
1 204
 
27.7%
2 6
 
0.8%

Length

2024-03-25T14:59:37.532725image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:37.703384image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 526
71.5%
1 204
 
27.7%
2 6
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 526
71.5%
1 204
 
27.7%
2 6
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 526
71.5%
1 204
 
27.7%
2 6
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 526
71.5%
1 204
 
27.7%
2 6
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 526
71.5%
1 204
 
27.7%
2 6
 
0.8%

orphanhood
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
417 
2
254 
1
65 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 417
56.7%
2 254
34.5%
1 65
 
8.8%

Length

2024-03-25T14:59:37.887913image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:38.049870image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 417
56.7%
2 254
34.5%
1 65
 
8.8%

Most occurring characters

ValueCountFrequency (%)
0 417
56.7%
2 254
34.5%
1 65
 
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 417
56.7%
2 254
34.5%
1 65
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 417
56.7%
2 254
34.5%
1 65
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 417
56.7%
2 254
34.5%
1 65
 
8.8%

ses_cat
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
404 
1
332 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 404
54.9%
1 332
45.1%

Length

2024-03-25T14:59:38.233517image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:38.381819image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 404
54.9%
1 332
45.1%

Most occurring characters

ValueCountFrequency (%)
0 404
54.9%
1 332
45.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 404
54.9%
1 332
45.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 404
54.9%
1 332
45.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 404
54.9%
1 332
45.1%

livelihood1
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
703 
1
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 703
95.5%
1 33
 
4.5%

Length

2024-03-25T14:59:38.550074image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:38.719746image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 703
95.5%
1 33
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 703
95.5%
1 33
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 703
95.5%
1 33
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 703
95.5%
1 33
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 703
95.5%
1 33
 
4.5%

sexualever1
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
729 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 729
99.0%
1 7
 
1.0%

Length

2024-03-25T14:59:38.882781image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:39.231204image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 729
99.0%
1 7
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 729
99.0%
1 7
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 729
99.0%
1 7
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 729
99.0%
1 7
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 729
99.0%
1 7
 
1.0%

childstay1
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2241848
Minimum0
Maximum5
Zeros207
Zeros (%)28.1%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-03-25T14:59:39.383732image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.816032
Coefficient of variation (CV)0.8164933
Kurtosis-1.3837472
Mean2.2241848
Median Absolute Deviation (MAD)2
Skewness0.13995737
Sum1637
Variance3.2979721
MonotonicityNot monotonic
2024-03-25T14:59:39.546915image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 207
28.1%
2 129
17.5%
4 123
16.7%
5 108
14.7%
3 89
12.1%
1 80
 
10.9%
ValueCountFrequency (%)
0 207
28.1%
1 80
 
10.9%
2 129
17.5%
3 89
12.1%
4 123
16.7%
5 108
14.7%
ValueCountFrequency (%)
5 108
14.7%
4 123
16.7%
3 89
12.1%
2 129
17.5%
1 80
 
10.9%
0 207
28.1%

childartk1
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
1
707 
0
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 707
96.1%
0 29
 
3.9%

Length

2024-03-25T14:59:39.717224image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:39.884695image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 707
96.1%
0 29
 
3.9%

Most occurring characters

ValueCountFrequency (%)
1 707
96.1%
0 29
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 707
96.1%
0 29
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 707
96.1%
0 29
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 707
96.1%
0 29
 
3.9%

childworst1
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
1
396 
2
207 
0
113 
3
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 396
53.8%
2 207
28.1%
0 113
 
15.4%
3 20
 
2.7%

Length

2024-03-25T14:59:40.048286image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:40.330400image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 396
53.8%
2 207
28.1%
0 113
 
15.4%
3 20
 
2.7%

Most occurring characters

ValueCountFrequency (%)
1 396
53.8%
2 207
28.1%
0 113
 
15.4%
3 20
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 396
53.8%
2 207
28.1%
0 113
 
15.4%
3 20
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 396
53.8%
2 207
28.1%
0 113
 
15.4%
3 20
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 396
53.8%
2 207
28.1%
0 113
 
15.4%
3 20
 
2.7%

childpremt1
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
2
688 
0
 
32
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 688
93.5%
0 32
 
4.3%
1 16
 
2.2%

Length

2024-03-25T14:59:40.518558image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:40.681206image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2 688
93.5%
0 32
 
4.3%
1 16
 
2.2%

Most occurring characters

ValueCountFrequency (%)
2 688
93.5%
0 32
 
4.3%
1 16
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 688
93.5%
0 32
 
4.3%
1 16
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 688
93.5%
0 32
 
4.3%
1 16
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 688
93.5%
0 32
 
4.3%
1 16
 
2.2%

chilborhiv
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
2
701 
1
 
22
0
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 701
95.2%
1 22
 
3.0%
0 13
 
1.8%

Length

2024-03-25T14:59:40.882690image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:41.113858image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2 701
95.2%
1 22
 
3.0%
0 13
 
1.8%

Most occurring characters

ValueCountFrequency (%)
2 701
95.2%
1 22
 
3.0%
0 13
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 701
95.2%
1 22
 
3.0%
0 13
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 701
95.2%
1 22
 
3.0%
0 13
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 701
95.2%
1 22
 
3.0%
0 13
 
1.8%

Stress
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
279 
1
234 
2
223 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
0 279
37.9%
1 234
31.8%
2 223
30.3%

Length

2024-03-25T14:59:41.299178image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:41.515406image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 279
37.9%
1 234
31.8%
2 223
30.3%

Most occurring characters

ValueCountFrequency (%)
0 279
37.9%
1 234
31.8%
2 223
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 279
37.9%
1 234
31.8%
2 223
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 279
37.9%
1 234
31.8%
2 223
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 279
37.9%
1 234
31.8%
2 223
30.3%

GroupCategory
Categorical

UNIFORM 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
368 
1
368 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 368
50.0%
1 368
50.0%

Length

2024-03-25T14:59:41.698819image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:41.861909image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 368
50.0%
1 368
50.0%

Most occurring characters

ValueCountFrequency (%)
0 368
50.0%
1 368
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 368
50.0%
1 368
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 368
50.0%
1 368
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 368
50.0%
1 368
50.0%

CD4_category
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
586 
2
76 
1
74 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 586
79.6%
2 76
 
10.3%
1 74
 
10.1%

Length

2024-03-25T14:59:42.031025image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:42.250231image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 586
79.6%
2 76
 
10.3%
1 74
 
10.1%

Most occurring characters

ValueCountFrequency (%)
0 586
79.6%
2 76
 
10.3%
1 74
 
10.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 586
79.6%
2 76
 
10.3%
1 74
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 586
79.6%
2 76
 
10.3%
1 74
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 586
79.6%
2 76
 
10.3%
1 74
 
10.1%
Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
1
356 
0
229 
2
151 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 356
48.4%
0 229
31.1%
2 151
20.5%

Length

2024-03-25T14:59:42.434065image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:42.595923image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 356
48.4%
0 229
31.1%
2 151
20.5%

Most occurring characters

ValueCountFrequency (%)
1 356
48.4%
0 229
31.1%
2 151
20.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 356
48.4%
0 229
31.1%
2 151
20.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 356
48.4%
0 229
31.1%
2 151
20.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 356
48.4%
0 229
31.1%
2 151
20.5%

tlbase
Real number (ℝ)

Distinct613
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52213855
Minimum0
Maximum1
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-03-25T14:59:42.796304image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.28562818
Q10.43239867
median0.52213855
Q30.60305807
95-th percentile0.79507187
Maximum1
Range1
Interquartile range (IQR)0.1706594

Descriptive statistics

Standard deviation0.15309919
Coefficient of variation (CV)0.29321564
Kurtosis1.1750897
Mean0.52213855
Median Absolute Deviation (MAD)0.082920603
Skewness0.038931822
Sum384.29398
Variance0.023439362
MonotonicityNot monotonic
2024-03-25T14:59:43.029116image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5221385542 123
 
16.7%
0.7543137756 2
 
0.3%
0.407429005 1
 
0.1%
0.345350066 1
 
0.1%
0.8834426023 1
 
0.1%
0.7774557581 1
 
0.1%
0.5511416914 1
 
0.1%
0.6210148242 1
 
0.1%
0.7446550342 1
 
0.1%
0.6128104472 1
 
0.1%
Other values (603) 603
81.9%
ValueCountFrequency (%)
0 1
0.1%
0.03539486515 1
0.1%
0.03978523881 1
0.1%
0.0414360193 1
0.1%
0.04267504439 1
0.1%
0.04539599918 1
0.1%
0.05657837415 1
0.1%
0.05675954308 1
0.1%
0.06617432122 1
0.1%
0.07409557022 1
0.1%
ValueCountFrequency (%)
1 1
0.1%
0.9772732392 1
0.1%
0.9583680212 1
0.1%
0.9567009117 1
0.1%
0.9563934216 1
0.1%
0.9528581928 1
0.1%
0.9461461085 1
0.1%
0.9368332764 1
0.1%
0.9320145544 1
0.1%
0.9239978708 1
0.1%

stin2vntr_
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
2
444 
1
238 
0
54 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row0
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 444
60.3%
1 238
32.3%
0 54
 
7.3%

Length

2024-03-25T14:59:43.282212image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:43.444475image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2 444
60.3%
1 238
32.3%
0 54
 
7.3%

Most occurring characters

ValueCountFrequency (%)
2 444
60.3%
1 238
32.3%
0 54
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 444
60.3%
1 238
32.3%
0 54
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 444
60.3%
1 238
32.3%
0 54
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 444
60.3%
1 238
32.3%
0 54
 
7.3%

httlpr1
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
492 
1
209 
2
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 492
66.8%
1 209
28.4%
2 35
 
4.8%

Length

2024-03-25T14:59:43.628581image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:43.777629image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 492
66.8%
1 209
28.4%
2 35
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 492
66.8%
1 209
28.4%
2 35
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 492
66.8%
1 209
28.4%
2 35
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 492
66.8%
1 209
28.4%
2 35
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 492
66.8%
1 209
28.4%
2 35
 
4.8%

HTTLPRrs35531
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3831522
Minimum0
Maximum6
Zeros317
Zeros (%)43.1%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-03-25T14:59:43.933006image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7449445
Coefficient of variation (CV)1.2615709
Kurtosis0.84305341
Mean1.3831522
Median Absolute Deviation (MAD)1
Skewness1.3640233
Sum1018
Variance3.0448314
MonotonicityNot monotonic
2024-03-25T14:59:44.093038image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 317
43.1%
1 158
21.5%
2 150
20.4%
5 54
 
7.3%
6 32
 
4.3%
4 23
 
3.1%
3 2
 
0.3%
ValueCountFrequency (%)
0 317
43.1%
1 158
21.5%
2 150
20.4%
3 2
 
0.3%
4 23
 
3.1%
5 54
 
7.3%
6 32
 
4.3%
ValueCountFrequency (%)
6 32
 
4.3%
5 54
 
7.3%
4 23
 
3.1%
3 2
 
0.3%
2 150
20.4%
1 158
21.5%
0 317
43.1%

rs35531
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
499 
1
214 
2
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 499
67.8%
1 214
29.1%
2 23
 
3.1%

Length

2024-03-25T14:59:44.290056image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:44.442981image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 499
67.8%
1 214
29.1%
2 23
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 499
67.8%
1 214
29.1%
2 23
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 499
67.8%
1 214
29.1%
2 23
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 499
67.8%
1 214
29.1%
2 23
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 499
67.8%
1 214
29.1%
2 23
 
3.1%

Rs10482605
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
2
482 
3
133 
1
75 
0
 
37
4
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 482
65.5%
3 133
 
18.1%
1 75
 
10.2%
0 37
 
5.0%
4 9
 
1.2%

Length

2024-03-25T14:59:44.625243image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:44.792459image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2 482
65.5%
3 133
 
18.1%
1 75
 
10.2%
0 37
 
5.0%
4 9
 
1.2%

Most occurring characters

ValueCountFrequency (%)
2 482
65.5%
3 133
 
18.1%
1 75
 
10.2%
0 37
 
5.0%
4 9
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 482
65.5%
3 133
 
18.1%
1 75
 
10.2%
0 37
 
5.0%
4 9
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 482
65.5%
3 133
 
18.1%
1 75
 
10.2%
0 37
 
5.0%
4 9
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 482
65.5%
3 133
 
18.1%
1 75
 
10.2%
0 37
 
5.0%
4 9
 
1.2%

Rs1360780
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
3
320 
2
254 
4
111 
0
37 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3 320
43.5%
2 254
34.5%
4 111
 
15.1%
0 37
 
5.0%
1 14
 
1.9%

Length

2024-03-25T14:59:44.989250image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:45.158745image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
3 320
43.5%
2 254
34.5%
4 111
 
15.1%
0 37
 
5.0%
1 14
 
1.9%

Most occurring characters

ValueCountFrequency (%)
3 320
43.5%
2 254
34.5%
4 111
 
15.1%
0 37
 
5.0%
1 14
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 320
43.5%
2 254
34.5%
4 111
 
15.1%
0 37
 
5.0%
1 14
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 320
43.5%
2 254
34.5%
4 111
 
15.1%
0 37
 
5.0%
1 14
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 320
43.5%
2 254
34.5%
4 111
 
15.1%
0 37
 
5.0%
1 14
 
1.9%

rs1386494
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
4
412 
3
249 
0
 
38
2
 
24
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 412
56.0%
3 249
33.8%
0 38
 
5.2%
2 24
 
3.3%
1 13
 
1.8%

Length

2024-03-25T14:59:45.386324image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:45.592797image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
4 412
56.0%
3 249
33.8%
0 38
 
5.2%
2 24
 
3.3%
1 13
 
1.8%

Most occurring characters

ValueCountFrequency (%)
4 412
56.0%
3 249
33.8%
0 38
 
5.2%
2 24
 
3.3%
1 13
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 412
56.0%
3 249
33.8%
0 38
 
5.2%
2 24
 
3.3%
1 13
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 412
56.0%
3 249
33.8%
0 38
 
5.2%
2 24
 
3.3%
1 13
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 412
56.0%
3 249
33.8%
0 38
 
5.2%
2 24
 
3.3%
1 13
 
1.8%

rs1843809
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
3
319 
4
218 
2
149 
0
38 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 319
43.3%
4 218
29.6%
2 149
20.2%
0 38
 
5.2%
1 12
 
1.6%

Length

2024-03-25T14:59:45.845221image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:46.111982image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
3 319
43.3%
4 218
29.6%
2 149
20.2%
0 38
 
5.2%
1 12
 
1.6%

Most occurring characters

ValueCountFrequency (%)
3 319
43.3%
4 218
29.6%
2 149
20.2%
0 38
 
5.2%
1 12
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 319
43.3%
4 218
29.6%
2 149
20.2%
0 38
 
5.2%
1 12
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 319
43.3%
4 218
29.6%
2 149
20.2%
0 38
 
5.2%
1 12
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 319
43.3%
4 218
29.6%
2 149
20.2%
0 38
 
5.2%
1 12
 
1.6%

rs34517220
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
3
336 
4
175 
2
174 
0
38 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters736
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3 336
45.7%
4 175
23.8%
2 174
23.6%
0 38
 
5.2%
1 13
 
1.8%

Length

2024-03-25T14:59:46.385005image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T14:59:46.606354image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
3 336
45.7%
4 175
23.8%
2 174
23.6%
0 38
 
5.2%
1 13
 
1.8%

Most occurring characters

ValueCountFrequency (%)
3 336
45.7%
4 175
23.8%
2 174
23.6%
0 38
 
5.2%
1 13
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 336
45.7%
4 175
23.8%
2 174
23.6%
0 38
 
5.2%
1 13
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 336
45.7%
4 175
23.8%
2 174
23.6%
0 38
 
5.2%
1 13
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 336
45.7%
4 175
23.8%
2 174
23.6%
0 38
 
5.2%
1 13
 
1.8%

Interactions

2024-03-25T14:59:31.337029image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:25.828747image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:26.812282image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:27.819153image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:28.805243image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:29.752455image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:31.541635image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:25.999891image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:26.961890image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:27.981204image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:28.965743image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:29.912451image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:31.760659image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:26.149680image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:27.133369image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:28.126725image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:29.109708image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:30.083651image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:31.948895image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:26.306984image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:27.350374image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:28.277170image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:29.279138image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:30.243996image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:32.142028image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:26.445933image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:27.506854image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:28.433040image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:29.428238image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:30.907983image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:32.292563image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:26.613657image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:27.644537image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:28.648882image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:29.596242image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-03-25T14:59:31.095715image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2024-03-25T14:59:46.843421image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
AgeCategoryBMI_categoryCD4_categoryGroupCategoryHTTLPRrs35531Rs10482605Rs1360780StressViralload_Categoryagecatakchilborhivchildartk1childeduc1childpremt1childstay1childtrib1childworst1heightst1httlpr1livelihood1orphanhoodreligion1rs1386494rs1843809rs34517220rs35531ses_catsex1sexualever1stin2vntr_tlbaseweightst1
AgeCategory1.0000.1570.1200.0000.0490.0580.0120.0970.1490.9990.0000.0000.1560.0000.0090.0000.033-0.4040.0000.0650.0960.0220.0000.0270.0930.0000.0000.0000.0470.034-0.034-0.431
BMI_category0.1571.0000.1150.000-0.0070.0000.0000.0520.1310.1910.0540.0000.2940.0770.0150.0000.000-0.3090.0480.0560.122-0.0780.0500.0000.0000.0000.0860.0930.0870.000-0.029-0.560
CD4_category0.1200.1151.0000.035-0.0040.0000.0000.0000.2340.1000.0000.0000.1980.029-0.0170.0000.0800.2530.0000.0680.0650.0070.0000.0000.0000.0000.0000.0300.0000.0270.0230.275
GroupCategory0.0000.0000.0351.000-0.0570.1640.1290.1230.0570.0000.0150.0590.0000.037-0.0290.0000.085-0.0240.0550.0000.000-0.0100.1320.1790.1030.0000.0000.0440.0760.000-0.144-0.019
HTTLPRrs355310.049-0.007-0.004-0.0571.0000.1210.1130.0000.0000.0180.0000.0000.1080.036-0.0250.0570.000-0.0090.9460.0000.0550.0240.1300.1120.1250.9970.0710.0000.0000.272-0.048-0.020
Rs104826050.0580.0000.0000.1640.1211.0000.5140.1140.0000.0510.0000.0000.0000.0350.0950.0380.054-0.0560.0980.0000.044-0.0390.5100.5120.5320.0990.0000.0000.0000.110-0.086-0.057
Rs13607800.0120.0000.0000.1290.1130.5141.0000.0620.0000.0000.0000.0000.0390.0530.0230.0000.058-0.0780.1010.0160.0000.0050.5370.5040.5370.0890.0000.0000.0000.126-0.027-0.071
Stress0.0970.0520.0000.1230.0000.1140.0621.0000.0570.0910.0080.0000.0000.057-0.0440.0000.0430.1400.0570.0000.000-0.0210.0240.0550.0440.0000.0000.0000.0790.0310.0170.167
Viralload_Category0.1490.1310.2340.0570.0000.0000.0000.0571.0000.1180.0460.1890.0680.007-0.0410.0000.0660.0710.0000.0540.023-0.0490.0350.0000.0000.0000.0890.0000.0180.0750.0670.082
agecatak0.9990.1910.1000.0000.0180.0510.0000.0910.1181.0000.0300.0000.2040.0000.0370.0130.060-0.1620.0000.1600.109-0.0110.0000.0000.0450.0120.0110.0000.1140.010-0.011-0.184
chilborhiv0.0000.0540.0000.0150.0000.0000.0000.0080.0460.0301.0000.0760.0300.000-0.0040.0000.000-0.0780.0230.0000.000-0.0180.0850.0550.0050.0000.0000.0430.0410.0000.015-0.081
childartk10.0000.0000.0000.0590.0000.0000.0000.0000.1890.0000.0761.0000.0000.0480.0390.0340.0240.0320.0000.0000.000-0.0250.0000.0000.0300.0000.0310.0000.0000.000-0.0070.036
childeduc10.1560.2940.1980.0000.1080.0000.0390.0000.0680.2040.0300.0001.0000.000-0.0440.0280.0000.3750.0000.1090.0200.0270.0260.0000.0000.0000.1010.0730.0000.0000.0230.429
childpremt10.0000.0770.0290.0370.0360.0350.0530.0570.0070.0000.0000.0480.0001.000-0.0270.0480.0000.0190.0420.0000.0730.0350.0000.0000.0750.0420.0700.0000.0000.000-0.0040.000
childstay10.0090.015-0.017-0.029-0.0250.0950.023-0.044-0.0410.037-0.0040.039-0.044-0.0271.0000.1520.153-0.0890.0630.0000.0560.0060.0320.0000.0000.0000.2250.0640.0590.000-0.065-0.067
childtrib10.0000.0000.0000.0000.0570.0380.0000.0000.0000.0130.0000.0340.0280.0480.1521.0000.0450.0070.0000.0000.0000.0520.0000.0000.0480.0380.0480.0640.0000.000-0.000-0.006
childworst10.0330.0000.0800.0850.0000.0540.0580.0430.0660.0600.0000.0240.0000.0000.1530.0451.0000.0290.0000.0000.0000.0240.0770.0560.0640.0000.0000.0000.0140.0000.1000.016
heightst1-0.404-0.3090.253-0.024-0.009-0.056-0.0780.1400.071-0.162-0.0780.0320.3750.019-0.0890.0070.0291.0000.0000.1000.1480.0040.0000.0350.0000.0000.1640.0700.0410.0000.0510.875
httlpr10.0000.0480.0000.0550.9460.0980.1010.0570.0000.0000.0230.0000.0000.0420.0630.0000.0000.0001.0000.0070.0550.0480.1090.0930.1010.1360.0000.0000.0000.063-0.068-0.004
livelihood10.0650.0560.0680.0000.0000.0000.0160.0000.0540.1600.0000.0000.1090.0000.0000.0000.0000.1000.0071.0000.072-0.0080.0000.0000.0000.0000.0000.0190.0000.0000.0050.119
orphanhood0.0960.1220.0650.0000.0550.0440.0000.0000.0230.1090.0000.0000.0200.0730.0560.0000.0000.1480.0550.0721.000-0.0130.0000.0000.0000.0000.0000.0550.1060.062-0.0190.169
religion10.022-0.0780.007-0.0100.024-0.0390.005-0.021-0.049-0.011-0.018-0.0250.0270.0350.0060.0520.0240.0040.048-0.008-0.0131.0000.0500.0000.0680.0000.0460.0350.0880.0000.0190.007
rs13864940.0000.0500.0000.1320.1300.5100.5370.0240.0350.0000.0850.0000.0260.0000.0320.0000.0770.0000.1090.0000.0000.0501.0000.6140.6110.0810.0000.0290.1290.113-0.010-0.065
rs18438090.0270.0000.0000.1790.1120.5120.5040.0550.0000.0000.0550.0000.0000.0000.0000.0000.0560.0350.0930.0000.0000.0000.6141.0000.5590.0950.0520.0000.0000.118-0.039-0.060
rs345172200.0930.0000.0000.1030.1250.5320.5370.0440.0000.0450.0050.0300.0000.0750.0000.0480.0640.0000.1010.0000.0000.0680.6110.5591.0000.0920.0000.0000.0090.120-0.028-0.010
rs355310.0000.0000.0000.0000.9970.0990.0890.0000.0000.0120.0000.0000.0000.0420.0000.0380.0000.0000.1360.0000.0000.0000.0810.0950.0921.0000.0280.0000.0000.2480.015-0.046
ses_cat0.0000.0860.0000.0000.0710.0000.0000.0000.0890.0110.0000.0310.1010.0700.2250.0480.0000.1640.0000.0000.0000.0460.0000.0520.0000.0281.0000.0000.0290.0000.004-0.180
sex10.0000.0930.0300.0440.0000.0000.0000.0000.0000.0000.0430.0000.0730.0000.0640.0640.0000.0700.0000.0190.0550.0350.0290.0000.0000.0000.0001.0000.0000.0330.004-0.029
sexualever10.0470.0870.0000.0760.0000.0000.0000.0790.0180.1140.0410.0000.0000.0000.0590.0000.0140.0410.0000.0000.1060.0880.1290.0000.0090.0000.0290.0001.0000.0000.0200.110
stin2vntr_0.0340.0000.0270.0000.2720.1100.1260.0310.0750.0100.0000.0000.0000.0000.0000.0000.0000.0000.0630.0000.0620.0000.1130.1180.1200.2480.0000.0330.0001.000-0.013-0.034
tlbase-0.034-0.0290.023-0.144-0.048-0.086-0.0270.0170.067-0.0110.015-0.0070.023-0.004-0.065-0.0000.1000.051-0.0680.005-0.0190.019-0.010-0.039-0.0280.0150.0040.0040.020-0.0131.0000.049
weightst1-0.431-0.5600.275-0.019-0.020-0.057-0.0710.1670.082-0.184-0.0810.0360.4290.000-0.067-0.0060.0160.875-0.0040.1190.1690.007-0.065-0.060-0.010-0.046-0.180-0.0290.110-0.0340.0491.000

Missing values

2024-03-25T14:59:32.659108image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-25T14:59:33.437129image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agecatakAgeCategorysex1religion1childeduc1heightst1weightst1BMI_categorychildtrib1orphanhoodses_catlivelihood1sexualever1childstay1childartk1childworst1childpremt1chilborhivStressGroupCategoryCD4_categoryViralload_Categorytlbasestin2vntr_httlpr1HTTLPRrs35531rs35531Rs10482605Rs1360780rs1386494rs1843809rs34517220
0200010.2929830.1468533120000122200010.493433220023222
1001410.3002170.1748250121100122220020.549427226023222
2200210.2594650.0629373000104122211120.570102001133332
3110110.2085850.0349653100000112221000.770106215122332
4200210.2871960.1398603000004122210010.514873200022433
5111210.2712800.1118883000000122221000.000000112022434
6111110.2592240.0559443000000122211000.447273200022442
7110110.2447550.0629373000000122211010.559123212023433
8001110.3267420.2097903000000112220010.522139212012414
9110110.2688690.1188813101004122221020.633916100023332
agecatakAgeCategorysex1religion1childeduc1heightst1weightst1BMI_categorychildtrib1orphanhoodses_catlivelihood1sexualever1childstay1childartk1childworst1childpremt1chilborhivStressGroupCategoryCD4_categoryViralload_Categorytlbasestin2vntr_httlpr1HTTLPRrs35531rs35531Rs10482605Rs1360780rs1386494rs1843809rs34517220
726201510.2785150.0979023020002112200000.464782112022443
727201110.3074510.1678323000002122200000.353494101132434
728111110.2695920.0979023021002112210020.496510200023333
729000410.3291540.2027973011001122201000.522139200000000
730110110.2592240.0699303101005112201010.617106100022332
731110110.2406560.0489513100005112200010.416810112022323
732200110.2592240.0699303001002112200010.408469026033443
733200110.2411380.0629373000002112211110.546656200023434
734201220.3098630.1468533001003122200120.522139212014122
735110110.2507840.0769233000002112201000.324262200033443